Investigation of ica algorithms for feature extraction of EEG signals in discrimination of alzheimer disease

BIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing

Volumn 1, Issue , 2008, Pages 232-235

  Solé Casals, Jordi ^a   Vialatte, François ^b   Chen, Zhe ^b   Cichocki, Andrzej ^b  

^a UNIVERSITY OF VIC CENTRAL UNIVERSITY OF CATALONIA   (Spain)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

Alzheimer disease; BSS; EEG; Feature extraction; ICA

Indexed keywords

ALZHEIMER DISEASE; BSS; COGNITIVE IMPAIRMENT; CONTROL SUBJECT; EARLY DETECTION; EEG; EEG SIGNALS; ICA; ICA ALGORITHMS; NOISE REDUCTIONS; QUANTITATIVE COMPARISON;

APARTMENT HOUSES; BLIND SOURCE SEPARATION; DISEASE CONTROL; ELECTROENCEPHALOGRAPHY; HEMODYNAMICS; INDEPENDENT COMPONENT ANALYSIS; SIGNAL PROCESSING; SPEECH ANALYSIS;

FEATURE EXTRACTION;

EID: 67650516593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (3)

1. Cichocki, A. and Amari, 5. (2002). Adaptive Blind Signal and Image Processing. Wiley, New York.
2. Cichocki, A., Amari, S., and et al. Icalab toolboxes. http://www.bsp.brain.riken.jp/TCALAB.
3. Vialatte, F. and et al. (2005). Blind source separation and sparse bump modelling of time frequency representation of eeg signals: New tools for early detection of alzheimer's disease. In Proc. IEEE Work Machine Learning for Signal Processing, pp. 2 7-32.